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Found 11 papers, 0 papers with code
LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform
The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN).
Towards Robust 2D Convolution for Reliable Visual Recognition
2D convolution (Conv2d), which is responsible for extracting features from the input image, is one of the key modules of a convolutional neural network (CNN).
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images.
Ranked #5 on
Denoising
on Darmstadt Noise Dataset
A Probabilistic Collaborative Representation Based Approach for Pattern Classification
Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes.
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning
Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e. g., matching persons across ID photos and surveillance videos.
Convolutional Sparse Coding for Image Super-Resolution
Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR).
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior.
Projective dictionary pair learning for pattern classification
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems.
Weighted Nuclear Norm Minimization with Application to Image Denoising
In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights.
On the Optimal Solution of Weighted Nuclear Norm Minimization
Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.
Collaborative Representation based Classification for Face Recognition
It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored.
